Jan-Feb, 2024

Case Study: 'GoBoat'
website views analysis

Navigating Success with 'GoBoat', a European online seller platform
Optimizing marketing success through analyzing website views by key characteristics

(CF student case study)

'GoBoat' Portfolio Project Cover
Fig 1. Portfolio case study - intro cover

Goal

As a data analyst for a yacht and boat sales website, I've been tasked by the marketing team to analyze recent pricing and listing views data for their weekly newsletter. We're aiming to help sellers boost views and stay informed on market trends.

Here are the questions they want me to answer first:

1. What are the characteristics of the most viewed listings in the past week?
2. Do the priciest boats attract the most attention?
3. Are there common features among the top-viewed boats?

GoBoat' Portfolio Project Analysis and Workflow
Fig 2. Workflow of my analysis
GoBoat' Portfolio Project Analysis and Workflow
Fig 3. Insights gained from my analysis
GoBoat' Portfolio Project Analysis and Workflow
Fig 4. Final Recommendations

final thoughts

In conclusion, my case study underscores the importance of strategic approaches to enhance the visibility and search engine optimization (SEO) of GoBoat's online seller platform. Key attributes such as age, price, and condition should be highlighted to improve visibility and appeal to potential buyers. Segmenting boats across price ranges and types broadens the audience reach, while featuring popular keywords like diesel, materials, and brands in listings boosts SEO and attracts more views. By focusing marketing efforts on countries with high-viewed listings such as Switzerland, Germany, and Italy, GoBoat can increase regional stability and amplify its market presence. Additionally, sharing market trends empowers sellers to optimize listings and improve search rank, fostering a dynamic and competitive marketplace ecosystem.

Data:

Utilize open-source data from Kaggle, additional geographical data from geojson.
  • Kaggle - Boat Sales Data
  • Geographical Data

    • Data limitations and challenges:

    Approximately 5% of the entire dataset contained NAN values, which were scattered throughout and left as they were (to account for outliers) due to the absence of missing view counts. There was no confirmation of the metrics used in measurements, no purchase data, and no measurement for website views by time of day, month, or year. In retrospect, the most challenging aspect of this analysis was the cleaning of the open-sourced data, as this process was time-consuming and took approximately one week to complete before the data could be properly aggregated for an in-depth analysis.

  • View Final Results in Tableau Storyboard
  • View Full Data Documentation in GitHub

    • Thanks for following along!